A typical neural network contains ‘neurons’, which are connected by ‘weights’. Weights generally serve to communicate the results/conclusions reached by one neuron to other neurons. The weights are adjusted while examining a data set until the neural network models the system consistent with the data set (within an acceptable error level). In the share price example above, the weights of the neural network and the operation of neurons are adjusted until the share price of each day (in a data set) is predicted based on the price of the prior days (in the data set).
A prior neural network may be designed to start with random values for weights, and adjust the values iteratively while examining each data set. Various approaches may be used for such adjustment and example approaches are described in a document entitled, “An Introduction to Neural Networks”, available at the URL: http://www.cs.stir.ac.uk/˜lss/NNIntro/InvSlides.html.
One problem with starting with random weights for each system is that it may require a large number of iterations to determine weights, which would model a system at a desired level of accuracy. The resulting required large number of computations and large amount of time may be unacceptable in several environments. Accordingly, there is a general need to reduce the number of computations while modeling systems using neural networks.
In the drawings, like reference numbers generally indicate identical, functionally similar, and/or structurally similar elements. The drawing in which an element first appears is indicated by the leftmost digit(s) in the corresponding reference number.